Modern cyber-physical systems increasingly rely on computationally demanding applications, particularly at the edge, where Artificial Intelligence-based algorithms are deployed. To meet these demands, industry trends are shifting towards heterogeneous MultiProcessor Systems on Chip (MPSoCs), which must also satisfy strict real-time and functional safety requirements. A major challenge in such systems is memory contention, where multiple processing units compete for shared memory resources, affecting application performance and the accurate estimation of Worst-Case Execution Times (WCETs). Traditional static analysis becomes impractical as system configurations grow in complexity. This work presents the design of an analysis and optimization framework for real-time systems that re-evaluates WCET estimates based on system configurations to reflect the impact of memory contention on heterogeneous platforms. The proposed method estimates new WCETs using Quantile Regression Neural Networks (QRNNs), which infer memory contention from Event Monitor data. Experimental results reveal that QRNN models must be system-specific for accurate predictions and that memory access patterns significantly affect model generalization. Two strategies are proposed: using generic models for simplicity or task-specific models for higher accuracy. Despite some potential underestimations, QRNNs maintain a strong correlation with actual observed contention, enabling effective worst-case scenario identification. Furthermore, a comparative analysis highlights the superior scalability of the estimation-based approach over empirical measurements, especially in large system optimization processes where performance can be easily enhanced by at least two orders of magnitude, making it a practical solution for real-time system design and analysis.

Abstract The current trend in industrial applications is evolving towards heterogeneous platforms that integrate multiple processors and specialized accelerators within a single MPSoC (MultiProcessor System on Chip). Simultaneously, many of these applications have stringent timing requirements that must be met through the appropriate management of concurrency, synchronization, and the deployment of activities across the available computing elements in a distributed environment. In this context, Robot Operating System 2 (ROS 2) is gaining importance as a middleware for distributed systems in the automotive industry. This paper presents the real-time modeling and analysis of a smart mobility application running on a testbed based on MPSoC processors and ROS 2. While a comprehensive analysis of the use case under different configurations is presented, the main contribution is to provide the scientific community with a detailed generic model. This model serves as a benchmark for testing current and future techniques for the modeling, analysis, and optimization of real-time systems based on heterogeneous platforms while highlighting some challenges to be addressed.

The use of Ethernet and Linux is becoming common in industrial applications, even for those with real-time requirements, although neither of them were originally designed for this purpose. The emergence of Industry 4.0 (also known as Industrial Internet of Things, IIoT) has encouraged the evolution of these technologies to better handle real-time issues. On the one hand, Linux now supports mechanisms to configure certain real-time parameters, as well as core isolation and interrupt allocation facilities in multicore processors. On the other hand, the set of Ethernet standards IEEE 802.1 Time-Sensitive Networking (TSN) includes a high precision clock synchronization protocol (IEEE 802.1AS). The purpose of this work is to outline an execution framework for distributed systems based on TSN and Linux, which allows the execution of time-aware applications. We have studied and evaluated different configurations available for the proposed execution framework. In particular, a detailed characterization of the clock synchronization mechanism, from the application point of view, has been performed. Some conclusions about the current real-time capabilities of these technologies are also presented.

Standard distribution middleware has traditionally been perceived as complex software which is not suitable for satisfying the highest certification criteria in safety-critical environments. However, this idea is slowly changing and there are efforts such as the Future Airborne Capability Environment (FACE) consortium to integrate standard distribution middleware into the development of avionic systems. This integration facilitates the interoperability and portability of avionic applications, but there are still challenges that need to be addressed before full success can be achieved. To this end, this paper explores the usage of the Data Distribution Service for Real-Time Systems (DDS) on top of a partitioned system with a communication network based on the ARINC 664 specification (precisely, the AFDX network). This work specifically identifies the incompatibilities between the two standards and also proposes potential solutions. A set of overhead metrics of using DDS in a distributed partitioned platform is also provided.

The ever increasing computing demands in embedded systems is driving the adoption of hardware accelerators such as GPUs , which offer powerful platforms that can compute parallel workloads efficiently. Relevant critical applications that benefit from such platforms, for instance autonomous driving , usually impose additional real-time requirements that must be met to guarantee the correctness of the systems. In this paper, we propose exploiting readily available and extensively validated techniques to model and analyze real-time systems with GPUs . Specifically, we propose a methodology to employ the MAST model to characterize such systems, and different variants of the Offset-Based Response-Time Analysis techniques to validate the real-time requirements. We verify our approach with a real industrial application sourced from the railway industry . Through a comprehensive evaluation involving synthetic and real task-sets, we characterize the applicability of the approach, and we also show how estimated worst-case response times are aligned with real measurements up to 87.2%.

This paper considers the offline assignment of fixed priorities in partitioned preemptive real-time systems where tasks have precedence constraints. This problem is crucial in this type of systems, as having a good fixed priority assignment allows for an efficient use of the processing resources while meeting all the deadlines. In the literature, we can find several proposals to solve this problem, which offer varying trade-offs between the quality of their results and their computational complexities. In this paper, we propose a new approach, leveraging existing algorithms that are widely exploited in the field of Machine Learning: Gradient Descent, the Adam Optimizer, and Gradient Noise. We show how to adapt these algorithms to the problem of fixed priority assignment in conjunction with existing worst-case response time analyses. We demonstrate the performance of our proposal on synthetic task-sets with different sizes. This evaluation shows that our proposal is able to find more schedulable solutions than previous heuristics, approximating optimal but intractable algorithms such as MILP or brute-force, while requiring reasonable execution times.

We present here the main features and lessons learned from the first edition of what has now become the ECRTS industrial challenge, together with the final description of the challenge and a comparative overview of the proposed solutions. This verification challenge, proposed by Thales, was first discussed in 2014 as part of a dedicated workshop (FMTV, a satellite event of the FM 2014 conference), and solutions were discussed for the first time at the WATERS 2015 workshop. The use case for the verification challenge is an aerial video tracking system. A specificity of this system lies in the fact that periods are constant but known with a limited precision only. The first part of the challenge focuses on the video frame processing system. It consists in computing maximum values of the end-to-end latency of the frames sent by the camera to the display, for two different buffer sizes, and then the minimum duration between two consecutive frame losses. The second challenge is about computing end-to-end latencies on the tracking and camera control for two different values of jitter. Solutions based on five different tools - Fiacre/Tina, CPAL (simulation and analysis), IMITATOR, UPPAAL and MAST - were submitted for discussion at WATERS 2015. While none of these solutions provided a full answer to the challenge, a combination of several of them did allow to draw some conclusions.

The increasing complexity in the design of industrial embedded systems represents a challenge in the development of scheduling algorithms for such systems, which are essential to guarantee that they meet their deadlines even in the worst-case situation. In this work, we propose a new collection of non-iterative priority assignment algorithms for multipath flows within hierarchical schedulers based on state-of-the-art scheduling algorithms, which have been adapted to this complex system model. They are applied to an industrial railway use case that has motivated this work, and then their performance is evaluated in different general synthetic scenarios, with the aim of providing a view on how they behave in a wider range of system configurations.

Time and space partitioning techniques are implemented in the development of safety-critical applications to ensure isolation among components. A suitable arrangement of the execution of such partitions is a key challenge so that applications meet the timing requirements imposed to software. In this work, the effect of window sizes and context switch overheads in the partition window configuration is studied, with the aim of analyzing their impact when the response-time analysis and priority assignment techniques are applied. Then, a heuristic algorithm is proposed, in order to obtain a partition window configuration that enables the schedulability of partition-based safety critical systems. This algorithm is evaluated in synthetic test scenarios and it is also applied to a safety-critical use-case in the railway domain.

Existing response time analysis and optimization techniques for real-time distributed systems show that EDF schedulers feature better scheduling capabilities when a global clock reference can be used; this form of scheduling is known as global-clock EDF. In this context, precise clock synchronization is an enabling technology for future distributed real-time systems that want to leverage EDF scheduling. The IEEE 802.1AS protocol can be considered a stable technology for this purpose, as it is part of the Time-Sensitive Networking (TSN) family of standards to provide real-time communication over Ethernet. This paper presents a system architecture for applying global-clock EDF scheduling in distributed systems with soft real-time requirements. It also presents experiments to (1) assess the synchronization capabilities of the clock synchronization mechanism in the proposed architecture, (2) evaluate the performance of different scheduling deadline assignment techniques, and (3) contrast the theoretical results obtained by the schedulability analysis against those obtained through the execution of these experiments.

This paper develops an offset-based response-time analysis technique for analyzing complex distributed real-time systems where processing and communication resources use the time-partitioning strategy to isolate the operation of separate software components. Time partitioning may be provided in the processors by an ARINC 653 compliant operating system, and in the networks via the TTP communication protocol. The software components executed by the system may themselves be distributed and complex, composed of many concurrent tasks and with one or more end-to-end flows that may have end-to-end timing requirements. The developed analysis supports hierarchical scheduling where a primary scheduler performs time partitioning into separate partitions, and secondary fixed-priority schedulers dispatch the different concurrent tasks inside each partition. It also supports end-to-end flows that are either synchronized with the partition schedule or not. This is the first time that this kind of analysis is developed. An evaluation of an improvement introduced in the analysis is discussed. Two representative case studies are described.

Modern complex embedded systems are evolving into mixed-criticality systems in order to satisfy a wide set of non-functional requirements such as security, cost, weight, timing or power consumption. Partitioning is an enabling technology for this purpose, as it provides an environment with strong temporal and spatial isolation which allows the integration of applications with different requirements into a common hardware platform. At the same time, embedded systems are increasingly networked (e.g., cyber-physical systems) and they even might require global connectivity in open environments so enhanced communication mechanisms are needed to develop distributed partitioned systems. To this end, this work proposes an architecture to enable the use of data-centric real-time distribution middleware in partitioned embedded systems based on a hypervisor. This architecture relies on distribution middleware and a set of virtual devices to provide mixed-criticality partitions with a homogeneous and interoperable communication subsystem. The results obtained show that this approach provides low overhead and a reasonable trade-off between temporal isolation and performance.

Real-time distributed systems contain end-to-end flows, which are distributed actions composed of sequences of tasks activated through messages. Such flows usually have an end-to-end deadline but the internal tasks and messages do not have specific timing requirements. However, if EDF schedulers are used, it is necessary to assign scheduling deadlines to tasks and messages, which is usually done by distributing the end-to-end deadline among them. Distributed systems may have synchronized global clocks or non-synchronized local clocks. This work studies the influence of the clocks, global or local, on the deadline-assignment algorithms. A study on the poor performance observed for EDF schedulers with local clocks is presented. Then, a significant optimization of the assignment algorithms is shown, in which an amount of end-to-end deadline larger than the established timing requirement is distributed among tasks and messages. With this technique, two new algorithms for deadline-assignment are proposed, showing that they outperform the existing ones by up to 23 percent of processor utilization in the case of local clocks. Finally, the influence of release jitter in this kind of EDF systems and the positive effects of eliminating it are also studied.

The CAN bus is one of the most used networks in distributed real-time systems. The CAN bus can be divided by means of bridges in order to distribute the network load among different segments and contribute positively to the schedulability of the system. On the other hand, CAN supports the fragmentation of a message in multiple packets when it does not fit into one single CAN frame. The schedulability analysis of CAN has been extensively studied in the literature. Although the proposed methods analyze non-segmented CAN buses, they can be applied to segmented networks. However, the techniques from the related literature obtain quite pessimistic results in the analysis of segmented CAN networks with multi-packet messages. This paper proposes a less pessimistic CAN schedulability analysis method for the mentioned kind of networks. Moreover, this optimized technique can be integrated with methods oriented to the holistic schedulability analysis of a complete distributed real-time system.

The increasing acceptance of the Earliest Deadline First (EDF) scheduling algorithm in industrial environments, together with the continued usage of Fixed Priority (FP) scheduling is leading to heterogeneous systems with different scheduling policies in the same distributed system. Schedulability analysis techniques usually consider the entire system as a whole (holistic approach), with only one preestablished scheduling policy in all the resources. In this work, composition mechanisms will be presented that enable us to combine different FP and EDF response-time analysis techniques for checking the schedulability of heterogeneous systems. Additionally, priority and scheduling deadline assignment techniques will be combined into a new algorithm called HOSPA (Heuristic Optimized Scheduling Parameters Assignment), for optimizing the assignment of priorities and scheduling deadlines to tasks and messages in heterogeneous distributed hard real-time systems.

The assignment of fixed priorities to tasks and messages in distributed real-time systems is known to be an NP-hard problem, and thus there is no optimal method to accomplish it in polynomial time. This fact makes it a suitable problem to be approached with generic search and optimization algorithms. In this paper we propose a genetic algorithm with a permutational solution encoding for the assignment of fixed priorities to tasks and messages in distributed real-time systems using a holistic approach. This paper shows that the genetic algorithm can find more and better schedulable priority assignments than HO PA, which is, as far as we know, one of the best methods for the fixed priority assignment in distributed real-time systems.

This paper describes a model for representing the temporal and logical elements of real-time applications, called MAST. This model allows a very rich description of the system, including the effects of event or message-based synchronization, multiprocessor and distributed architectures as well as shared resource synchronization. The model is directly obtainable from a description of the system design using a UML tool. A system representation using this model is analyzable through a set of tools that has been developed within the MAST suite, including worst-case schedulability analysis for hard timing requirements, and discrete-event simulation for soft timing requirements. Although the current model only includes fixed priority systems, it is conceived as an open model and is easily extensible to accommodate other kinds of systems.

Presents a schedulability analysis technique for distributed hard real-time systems in which responses to different events may synchronize with each other. This technique uses a representation model for distributed systems that allows us to describe not only the task synchronization due to resource sharing, but also the activation due to combinations of events or the generation of several events by a single task. The model is representative of a large number of systems and is suitable for the treatment of message-passing systems or the client-server architecture. The analysis technique is based on the existing rate monotonic analysis (RMA) techniques for analyzing distributed real-time systems; it allows obtaining upper bounds for the worst-case response times of the system, thus allowing us to make guarantees about the fulfillment of the timing requirements that have been imposed.

We present an improvement of the schedulability analysis technique for distributed hard real time systems that allows us to increase the maximum schedulable resource utilization. Since the improvement affects only the analysis technique, there is no additional implementation cost for the application itself. The improvement in the analysis consists of calculating a lower bound for the best case response time of tasks and messages, in order to reduce the estimated jitter in the activation of subsequent tasks and messages. This reduction of jitter implies reduced worst case bounds for the response times, and thus allows us to increase the maximum schedulable utilization. The paper explores two different ways to calculate a lower bound on the best case execution times. The paper also shows the results of simulations that we have carried out, in which we found that we could increase the maximum schedulable limit of the different resources by approximately 5% more utilization.